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This paper mainly research the probabilistic planning problems based on the creating or destroying objects. Firstly, a new definition on mutex inference is given, because the objects can be created or destroyed and the actions are uncertainty. We adopt the thought of transforming object into proposition. Secondly, we propose a new concept of concurrent action set and give the algorithm for creating...
When dealing with massive quantities of data, top-k queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring function. The problem of efficiently answering such ranking queries has been studied and analyzed extensively within traditional database settings. The importance of the top-k is perhaps even greater in probabilistic databases, where...
Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes needs to handle new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records' scores induces a partial order over records, as opposed...
In this paper, we will study the problem of projected clustering of uncertain data streams. The use of uncertainty is especially important in the high dimensional scenario, because the sparsity property of high dimensional data is aggravated by the uncertainty. The uncertainty information is important for not only the determination of the assignment of data points to clusters, but also that of the...
Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, outdated sources and sampling errors. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, we propose a new rule-based classification and prediction algorithm called uRule for classifying uncertain...
Recent interest in managing uncertainty in data integration has led to the introduction of probabilistic schema mappings and the use of probabilistic methods to answer queries across multiple databases using two semantics: by-table and by-tuple. In this paper, we develop three possible semantics for aggregate queries: the range, distribution, and expected value semantics, and show that these three...
Risk is a widely acknowledged concept in the process of Transmission Planning. While planners obsess over risk and brain-storm to develop strategies to mitigate its effects, there is no industry standard - accepted or prescribed - that guides the planning process from a risk perspective. This is in part due to the intangible and subjective nature of risk, despite its formal definition. This intractability...
The measurement selection for updating the state estimate of a target's track, known as data association, is essential for good performance in the presence of spurious measurements or clutter. A classification of tracking and data association approaches has been presented, as a pure MMSE approach, which amounts to a soft decision, and single best-hypothesis approach, which amounts to a hard decision...
In a competitive power market, it is important to expand or strengthen transmission system in order to provide a fair environment to all market participants. In the present paper, a new method to determine an optimal transmission expansion plan in the deregulated power system has been proposed. In the proposed scheme, uncertainties, security index and transmission costs in calculation of LMPs for...
In many applications like moving-objects and sensors databases, data values are inherently uncertain. In these systems, an attribute value can be modeled as a range of possible values, associated with a probability density function. Data mining of the uncertain data attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability...
This paper is concerned with an approach of risk aversion by possibility theory. We introduce and study new possibilistic risk indicators. The main notions are the possibilistic risk premium and the possibilistic relative risk premium associated with a fuzzy number and a utility function. We also give formulae for computing them. They extend to possibility theory the classic notions of risk premium...
We propose a probabilistic extension of Allen's interval algebra for managing uncertain temporal relations. Although previous work on various uncertain forms of quantitative and qualitative temporal networks have been proposed in the literature, little has been addressed to the most obvious type of uncertainty, namely the probabilistic one. More precisely, our model adapts the probabilistic constraint...
Uncertain data are inherent in many important applications. Recently, considerable research efforts have been put into the field of managing uncertain data. In this paper, we summarize existing techniques to query and model uncertain data and systems that effectively manage uncertain data, mainly from a probabilistic point of view.
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